This article is like saying an apartment complex isn’t “losing money” because the monthly rents cover operating costs but ignoring the cost of the building. Most real estate developments go bust because the developers can’t pay the mortgage payment, not because they’re negative on operating costs.
If the cash flow was truly healthy these companies wouldn’t need to raise money. If you have healthy positive cash flow you have much better mechanisms available to fund capital investment other than selling shares at increasingly inflated valuations. Eg issue a bond against that healthy cash flow.
Fact remains when all costs are considered these companies are losing money and so long as the lifespan of a model is limited it’s going to stay ugly. Using that apartment building analogy it’s like having to knock down and rebuild the building every 6 months to stay relevant, but saying all is well because the rents cover the cost of garbage collection and the water bill. That’s simply not a viable business model.
Update Edit: A lot of commentary below re the R&D and training costs and if it’s fair to exclude that on inference costs or “unit economics.” I’d simply say inference is just selling compute and that should be high margin, which the article concludes it is. The issue behind the growing concerns about a giant AI bubble is if that margin is sufficient to cover the costs of everything else. I’d also say that excluding the cost of the model from “unit economics” calculations doesn’t make business/math/economics since it’s literally the thing being sold. It’s not some bit of fungible equipment or long term capital expense when they become obsolete after a few months. Take away the model and you’re just selling compute so it’s really not a great metric to use to say these companies are OK.